4.7 Article

Unravelling spatial gene associations with SEAGAL: a Python package for spatial transcriptomics data analysis and visualization

Related references

Note: Only part of the references are listed.
Review Biochemistry & Molecular Biology

Advances and Challenges in Spatial Transcriptomics for Developmental Biology

Kyongho Choe et al.

Summary: Development from single cells to multicellular tissues and organs involves differentiation, which is not merely the replication of cells. Previous research has focused on differences in gene expression profiles, but has been limited in understanding the molecular mechanisms of differentiation. Advancements in genomics, particularly single-cell RNA-sequencing (scRNA-seq), have enabled a better understanding of differentiation and cell lineage. However, the loss of spatial information during scRNA-seq has led to the emergence of spatial transcriptomics as a discipline and the development of tools to address this limitation.

BIOMOLECULES (2023)

Article Biochemical Research Methods

Squidpy: a scalable framework for spatial omics analysis

Giovanni Palla et al.

Summary: Squidpy is a Python framework that combines tools from omics and image analysis to efficiently store, manipulate, and visualize spatial omics data. It is extensible and can be interfaced with other libraries for scalable analysis of spatial omics data.

NATURE METHODS (2022)

Editorial Material Oncology

Spatial transcriptomics

Ana C. Anderson et al.

CANCER CELL (2022)

Editorial Material Biochemical Research Methods

Spatially resolved transcriptomics in neuroscience

Jennie L. Close et al.

Summary: Spatially resolved transcriptomics has great potential in unraveling the organization of brain cell types and their relationship with connectivity, circuit dynamics, behavior, and disease, but technical challenges need to be overcome to fully realize its potentials.

NATURE METHODS (2021)

Editorial Material Biochemical Research Methods

Method of the Year: spatially resolved transcriptomics

Vivien Marx

Summary: Nature Methods has named spatially resolved transcriptomics as Method of the Year 2020.

NATURE METHODS (2021)

Article Biotechnology & Applied Microbiology

Giotto: a toolbox for integrative analysis and visualization of spatial expression data

Ruben Dries et al.

Summary: Giotto is a comprehensive open-source toolbox for spatial data analysis and visualization, providing end-to-end analysis with a wide range of algorithms for characterizing tissue composition and cellular interactions, as well as integrating single-cell RNAseq data for cell-type enrichment analysis. The visualization module allows interactive visualization of analysis outputs and imaging features, demonstrating its general applicability across diverse datasets and platforms.

GENOME BIOLOGY (2021)

Article Multidisciplinary Sciences

Next-generation characterization of the Cancer Cell Line Encyclopedia

Mahmoud Ghandi et al.

NATURE (2019)

Article Biochemical Research Methods

SpatialDE: identification of spatially variable genes

Valentine Svensson et al.

NATURE METHODS (2018)

Article Biotechnology & Applied Microbiology

SCANPY: large-scale single-cell gene expression data analysis

F. Alexander Wolf et al.

GENOME BIOLOGY (2018)

Article Biochemical Research Methods

WGCNA: an R package for weighted correlation network analysis

Peter Langfelder et al.

BMC BIOINFORMATICS (2008)